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The proposed Postpartum Depression Analysis System has the user interface developed using Python's Streamlit library. The user interface is divided into two parts, the Postpartum Depression Test developed using HTML, CSS, JavaScript, PHP and Firebase, and the Data Analysis section developed using the Python-based Jupyter Notebook.

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Shash-cre-dev/HCI-Postpartum-Depression-Analysis-System

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HCI-Postpartum-Depression-Analysis-System

The proposed Postpartum Depression Analysis System has the user interface developed using Python's Streamlit library. The user interface is divided into two parts, the Postpartum Depression Test developed using HTML, CSS, JavaScript, PHP and Firebase, and the Data Analysis section developed using the Python-based Jupyter Notebook.

The Postpartum Depression Test has three pages altogether. On the first page, answers are made to ten multiple choice questions, each with four options, each having a weight of 0 to 3 depending on the response. More positive responses get assigned a weight of 0, whereas the least positive response will get assigned a weight of 3. These weights are later used to determine the total score and ascribe criteria ascribed to the user based on their answers. The second page collects demographic information about the user, which includes age range, gender, household income, country of residence, and questions about their mental health. The result of the criteria evaluated from the user's selected responses is exhibited on the third page as shown in Table 1. On page three, users have the option to receive their results via email. Additionally, if the total score falls between 14 and 30, the system suggests the nearest psychological centers and hospitals to the user's location.

The comprehensive data analysis is performed to on post-partum-data-set retrieved from Kaggle. In our research, we gathered a dataset of 1503 records from a medical hospital using a questionnaire administered through a Google form. This dataset has not yet been published. Our dataset includes 15 attributes, where we select 10 attributes (Timestamp, Age, Feeling sad or Tearful, Irritable towards baby & partner, Trouble sleeping at night, Problems concentrating or making decision, Overeating or loss of appetite, Feeling anxious, Feeling of guilt, Problems of bonding with baby, Suicide attempt) 9 of which were used (Timestamp being excluded) for analysis and 1 of which was the target attribute. The target attribute, "Feeling Anxious," was chosen as a predictor of postpartum depression.

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The proposed Postpartum Depression Analysis System has the user interface developed using Python's Streamlit library. The user interface is divided into two parts, the Postpartum Depression Test developed using HTML, CSS, JavaScript, PHP and Firebase, and the Data Analysis section developed using the Python-based Jupyter Notebook.

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